

'''simple docstring'''






import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
 import textwrap

 from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
 import torch

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 lowerCAmelCase__   		=      False


lowerCAmelCase__   		=      logging.get_logger(__name__)
lowerCAmelCase__   		=      '''ybelkada/fonts'''


def 						_A	(       ):






 """simple docstring"""



 if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
  raise ImportError(
      F"You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use "
      '''Pix2StructImageProcessor. Please upgrade torch.''' )


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 requires_backends(A__	,							['''torch'''] )
 _check_torch_version()

 __lowercase				    =					image_tensor.unsqueeze(0 )
 __lowercase				    =					torch.nn.functional.unfold(A__	,							(patch_height, patch_width)	,							stride=(patch_height, patch_width) )
 __lowercase				    =					patches.reshape(image_tensor.size(0 )	,							image_tensor.size(1 )	,							A__	,							A__	,							-1 )
 __lowercase				    =					patches.permute(0	,							4	,							2	,							3	,							1 ).reshape(
     image_tensor.size(2 ) // patch_height	,							image_tensor.size(3 ) // patch_width	,							image_tensor.size(1 ) * patch_height * patch_width	,							)
 return patches.unsqueeze(0 )


def 						_A	(       A__	,							A__ = 36	,							A__ = "black"	,							A__ = "white"	,							A__ = 5	,							A__ = 5	,							A__ = 5	,							A__ = 5	,							A__ = None	,							A__ = None	,							):






 """simple docstring"""



 requires_backends(A__	,							'''vision''' )
 # Add new lines so that each line is no more than 80 characters.

 __lowercase				    =					textwrap.TextWrapper(width=80 )
 __lowercase				    =					wrapper.wrap(text=A__ )
 __lowercase				    =					'''\n'''.join(A__ )

 if font_bytes is not None and font_path is None:
  __lowercase				    =					io.BytesIO(A__ )
 elif font_path is not None:
  __lowercase				    =					font_path
 else:
  __lowercase				    =					hf_hub_download(A__	,							'''Arial.TTF''' )
 __lowercase				    =					ImageFont.truetype(A__	,							encoding='''UTF-8'''	,							size=A__ )

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 __lowercase				    =					ImageDraw.Draw(Image.new('''RGB'''	,							(1, 1)	,							A__ ) )
 __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase				    =					temp_draw.textbbox((0, 0)	,							A__	,							A__ )

 # Create the actual image with a bit of padding around the text.
 __lowercase				    =					text_width + left_padding + right_padding
 __lowercase				    =					text_height + top_padding + bottom_padding
 __lowercase				    =					Image.new('''RGB'''	,							(image_width, image_height)	,							A__ )
 __lowercase				    =					ImageDraw.Draw(A__ )
 draw.text(xy=(left_padding, top_padding)	,							text=A__	,							fill=A__	,							font=A__ )
 return image


def 						_A	(       A__	,							A__	,							**A__ ):






 """simple docstring"""



 requires_backends(A__	,							'''vision''' )

 # Convert to PIL image if necessary
 __lowercase				    =					to_pil_image(A__ )

 __lowercase				    =					render_text(A__	,							**A__ )
 __lowercase				    =					max(header_image.width	,							image.width )

 __lowercase				    =					int(image.height * (new_width / image.width) )
 __lowercase				    =					int(header_image.height * (new_width / header_image.width) )

 __lowercase				    =					Image.new('''RGB'''	,							(new_width, new_height + new_header_height)	,							'''white''' )
 new_image.paste(header_image.resize((new_width, new_header_height) )	,							(0, 0) )
 new_image.paste(image.resize((new_width, new_height) )	,							(0, new_header_height) )

 # Convert back to the original framework if necessary
 __lowercase				    =					to_numpy_array(A__ )

 if infer_channel_dimension_format(A__ ) == ChannelDimension.LAST:
  __lowercase				    =					to_channel_dimension_format(A__	,							ChannelDimension.LAST )

 return new_image






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    List[str]							     =     ['flattened_patches']






  def __init__( self  :	Any   ,lowercase__  :	bool = True   ,lowercase__  :	bool = True   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	int = 2_0_4_8   ,lowercase__  :	bool = False   ,**lowercase__  :	List[str]   ,):
   super().__init__(**lowercase__     )
   __lowercase				    =					patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
   __lowercase				    =					do_normalize
   __lowercase				    =					do_convert_rgb
   __lowercase				    =					max_patches
   __lowercase				    =					is_vqa






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	np.ndarray   ,lowercase__  :	int   ,lowercase__  :	dict   ,**lowercase__  :	Tuple     ):
   requires_backends(self.extract_flattened_patches   ,'''torch'''     )
   _check_torch_version()

   # convert to torch
   __lowercase				    =					to_channel_dimension_format(lowercase__   ,ChannelDimension.FIRST     )
   __lowercase				    =					torch.from_numpy(lowercase__     )

   __lowercase    ,      __lowercase				    =					patch_size['''height'''], patch_size['''width''']
   __lowercase    ,      __lowercase				    =					get_image_size(lowercase__     )

   # maximize scale s.t.
   __lowercase				    =					math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)     )
   __lowercase				    =					max(min(math.floor(scale * image_height / patch_height     )   ,lowercase__     )   ,1     )
   __lowercase				    =					max(min(math.floor(scale * image_width / patch_width     )   ,lowercase__     )   ,1     )
   __lowercase				    =					max(num_feasible_rows * patch_height   ,1     )
   __lowercase				    =					max(num_feasible_cols * patch_width   ,1     )

   __lowercase				    =					torch.nn.functional.interpolate(
       image.unsqueeze(0     )   ,size=(resized_height, resized_width)   ,mode='''bilinear'''   ,align_corners=lowercase__   ,antialias=lowercase__   ,).squeeze(0     )

   # [1, rows, columns, patch_height * patch_width * image_channels]
   __lowercase				    =					torch_extract_patches(lowercase__   ,lowercase__   ,lowercase__     )

   __lowercase				    =					patches.shape
   __lowercase				    =					patches_shape[1]
   __lowercase				    =					patches_shape[2]
   __lowercase				    =					patches_shape[3]

   # [rows * columns, patch_height * patch_width * image_channels]
   __lowercase				    =					patches.reshape([rows * columns, depth]     )

   # [rows * columns, 1]
   __lowercase				    =					torch.arange(lowercase__     ).reshape([rows, 1]     ).repeat(1   ,lowercase__     ).reshape([rows * columns, 1]     )
   __lowercase				    =					torch.arange(lowercase__     ).reshape([1, columns]     ).repeat(lowercase__   ,1     ).reshape([rows * columns, 1]     )

   # Offset by 1 so the ids do not contain zeros, which represent padding.
   row_ids += 1
   col_ids += 1

   # Prepare additional patch features.
   # [rows * columns, 1]
   __lowercase				    =					row_ids.to(torch.floataa     )
   __lowercase				    =					col_ids.to(torch.floataa     )

   # [rows * columns, 2 + patch_height * patch_width * image_channels]
   __lowercase				    =					torch.cat([row_ids, col_ids, patches]   ,-1     )

   # [max_patches, 2 + patch_height * patch_width * image_channels]
   __lowercase				    =					torch.nn.functional.pad(lowercase__   ,[0, 0, 0, max_patches - (rows * columns)]     ).float()

   __lowercase				    =					to_numpy_array(lowercase__     )

   return result






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	np.ndarray   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	List[Any]     ):
   if image.dtype == np.uinta:
    __lowercase				    =					image.astype(np.floataa     )

   # take mean across the whole `image`
   __lowercase				    =					np.mean(lowercase__     )
   __lowercase				    =					np.std(lowercase__     )
   __lowercase				    =					max(lowercase__   ,1.0 / math.sqrt(np.prod(image.shape     )     )     )

   return normalize(lowercase__   ,mean=lowercase__   ,std=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	ImageInput   ,lowercase__  :	Optional[str] = None   ,lowercase__  :	bool = None   ,lowercase__  :	Optional[bool] = None   ,lowercase__  :	Optional[int] = None   ,lowercase__  :	Optional[Dict[str, int]] = None   ,lowercase__  :	Optional[Union[str, TensorType]] = None   ,lowercase__  :	ChannelDimension = ChannelDimension.FIRST   ,**lowercase__  :	List[Any]   ,):
   __lowercase				    =					do_normalize if do_normalize is not None else self.do_normalize
   __lowercase				    =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
   __lowercase				    =					patch_size if patch_size is not None else self.patch_size
   __lowercase				    =					max_patches if max_patches is not None else self.max_patches
   __lowercase				    =					self.is_vqa

   if kwargs.get('''data_format'''   ,lowercase__     ) is not None:
    raise ValueError('''data_format is not an accepted input as the outputs are '''     )

   __lowercase				    =					make_list_of_images(lowercase__     )

   if not valid_images(lowercase__     ):
    raise ValueError(
        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
        '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __lowercase				    =					[convert_to_rgb(lowercase__     ) for image in images]

   # All transformations expect numpy arrays.
   __lowercase				    =					[to_numpy_array(lowercase__     ) for image in images]

   if is_vqa:
    if header_text is None:
     raise ValueError('''A header text must be provided for VQA models.'''     )
    __lowercase				    =					kwargs.pop('''font_bytes'''   ,lowercase__     )
    __lowercase				    =					kwargs.pop('''font_path'''   ,lowercase__     )

    if isinstance(lowercase__   ,lowercase__     ):
     __lowercase				    =					[header_text] * len(lowercase__     )

    __lowercase				    =					[
        render_header(lowercase__   ,header_text[i]   ,font_bytes=lowercase__   ,font_path=lowercase__     )
        for i, image in enumerate(lowercase__     )
    ]

   if do_normalize:
    __lowercase				    =					[self.normalize(image=lowercase__     ) for image in images]

   # convert to torch tensor and permute
   __lowercase				    =					[
       self.extract_flattened_patches(image=lowercase__   ,max_patches=lowercase__   ,patch_size=lowercase__     )
       for image in images
   ]

   # create attention mask in numpy
   __lowercase				    =					[(image.sum(axis=-1     ) != 0).astype(np.floataa     ) for image in images]

   __lowercase				    =					BatchFeature(
       data={'''flattened_patches''': images, '''attention_mask''': attention_masks}   ,tensor_type=lowercase__     )

   return encoded_outputs



"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class 		a_    :







    def __init__(      self      :							Any  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Dict=13  ,       __UpperCamelCase      :							Tuple=7  ,       __UpperCamelCase      :							List[Any]=True  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							Union[str, Any]=False  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							str=99  ,       __UpperCamelCase      :							Optional[Any]=32  ,       __UpperCamelCase      :							Union[str, Any]=5  ,       __UpperCamelCase      :							Tuple=4  ,       __UpperCamelCase      :							Any=37  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							List[str]=5_12  ,       __UpperCamelCase      :							Union[str, Any]=16  ,       __UpperCamelCase      :							Union[str, Any]=2  ,       __UpperCamelCase      :							Union[str, Any]=0.0_2  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=4  ,       __UpperCamelCase      :							str=None  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_input_mask
        _UpperCAmelCase        = use_token_type_ids
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = type_sequence_label_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = num_choices
        _UpperCAmelCase        = scope







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.vocab_size  )

        _UpperCAmelCase        = None
        if self.use_input_mask:
            _UpperCAmelCase        = random_attention_mask([self.batch_size, self.seq_length]  )

        _UpperCAmelCase        = None
        if self.use_token_type_ids:
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.type_vocab_size  )

        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.type_sequence_label_size  )
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.num_labels  )
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.num_choices  )

        _UpperCAmelCase        = self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        return LlamaConfig(
            vocab_size=self.vocab_size  ,       hidden_size=self.hidden_size  ,       num_hidden_layers=self.num_hidden_layers  ,       num_attention_heads=self.num_attention_heads  ,       intermediate_size=self.intermediate_size  ,       hidden_act=self.hidden_act  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       max_position_embeddings=self.max_position_embeddings  ,       type_vocab_size=self.type_vocab_size  ,       is_decoder=__UpperCamelCase  ,       initializer_range=self.initializer_range  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.seq_length, self.vocab_size)  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Dict  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        # first forward pass
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       use_cache=__UpperCamelCase  ,       )
        _UpperCAmelCase        = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       vocab_size=2  )

        # append to next input_ids and
        _UpperCAmelCase        = torch.cat([input_ids, next_tokens]  ,       dim=-1  )
        _UpperCAmelCase        = torch.cat([input_mask, next_mask]  ,       dim=-1  )

        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       past_key_values=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]

        # select random slice
        _UpperCAmelCase        = ids_tensor((1,)  ,       output_from_past.shape[-1]  ).item()
        _UpperCAmelCase        = output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase        = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-3  )  )







    def   _snake_case       (      self      :							Tuple  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )        = config_and_inputs
        _UpperCAmelCase        = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    a   :					Any			     =					(LlamaForCausalLM,) if is_torch_available() else ()
    a   :					Union[str, Any]			     =					(
        {
            'feature-extraction': LlamaModel,
            'text-classification': LlamaForSequenceClassification,
            'text-generation': LlamaForCausalLM,
            'zero-shot': LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a   :					str			     =					False
    a   :					List[str]			     =					False







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _UpperCAmelCase        = type
            self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							Dict  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """single_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							List[str]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """multi_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""  )
    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        pass







    @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = ids_tensor([1, 10]  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,       config.vocab_size  )

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        original_model.to(__UpperCamelCase  )
        original_model.eval()
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = {"""type""": scaling_type, """factor""": 1_0.0}
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        scaled_model.to(__UpperCamelCase  )
        scaled_model.eval()
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )
        else:
            self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )







@require_torch
class 		a_    (			unittest.TestCase						):







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor([input_ids]  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]  )
        # fmt: on
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )







    @unittest.skip(
        """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )

        _UpperCAmelCase        = torch.tensor(
            [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]  ,       dtype=torch.floataa  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Model is curently gated"""  )
    @slow
    def   _snake_case       (      self      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
        _UpperCAmelCase        = """Simply put, the theory of relativity states that """
        _UpperCAmelCase        = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
        _UpperCAmelCase        = tokenizer.encode(__UpperCamelCase  ,       return_tensors="""pt"""  )
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained(
            """meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""sequential"""  ,       use_safetensors=__UpperCamelCase  )

        # greedy generation outputs
        _UpperCAmelCase        = model.generate(__UpperCamelCase  ,       max_new_tokens=64  ,       top_p=__UpperCamelCase  ,       temperature=1  ,       do_sample=__UpperCamelCase  )
        _UpperCAmelCase        = tokenizer.decode(generated_ids[0]  ,       skip_special_tokens=__UpperCamelCase  )
        self.assertEqual(__UpperCamelCase  ,       __UpperCamelCase  )